From the founders
Practical guides for AI rollout in engineering
Clear writing on approved workflows, human review, adoption measurement, and policy questions for teams moving AI from experiment to operating practice.
ViewGovernance / 9 min read
What an approved AI workflow looks like
The decisions that turn AI use from individual habit into a workflow your managers and reviewers can trust.
ViewGovernance / 9 min read
How to write an AI usage policy for engineering teams
A practical template for an AI coding policy your engineers will actually follow, covering approved tools, data rules, review, and accountability.
ViewEnablement / 8 min read
Choosing AI coding tools: a selection framework for engineering teams
How to evaluate and choose AI coding tools using criteria that survive a procurement review, not a feature demo.
ViewQuality / 8 min read
Managing AI-generated technical debt before it compounds
AI tools generate code faster than teams can maintain it. How to spot, price, and contain the technical debt that AI-assisted coding creates.
ViewCompliance / 9 min read
GDPR and AI coding tools: what engineering teams need to get right
A practical view of GDPR obligations when engineers use AI coding tools, covering personal data in prompts, processors, transfers, and DPAs.ViewQuality / 8 min read
AI code review at scale: keeping the bar high when volume goes up
How to review AI-generated pull requests without making review the bottleneck or rubber-stamping what the model wrote.
ViewEnablement / 8 min read
Onboarding engineers to AI tools without losing the fundamentals
An enablement model that gets engineers productive with AI tools quickly, without letting juniors skip the skills they still need to build.
ViewRisk / 8 min read
Shadow AI in engineering teams: find it before you govern it
Why unsanctioned AI use is the real starting point for governance, and how to bring it into scope without a crackdown.ViewSecurity / 9 min read
How to keep AI-assisted coding secure
A control model for secrets, insecure suggestions, and prompt injection when engineers code with AI.ViewOperations / 8 min read
How to measure AI adoption without fake ROI
A practical scorecard for proving adoption before making larger productivity claims.ViewRegulation / 10 min read
What the EU AI Act means for engineering leaders
A plain-language view of AI literacy, human oversight, and governance for teams using AI tools in engineering.
ViewStrategy / 8 min read
The ROI of AI coding tools: building a business case that holds up
How to build an ROI case for AI coding tools that survives a finance review, using costs and benefits you can defend instead of vendor productivity claims.
ViewSecurity / 8 min read
Do AI coding tools train on your code? What to verify before you roll out
A clear answer to whether AI coding tools train on your code, and the contract terms, settings, and data-handling questions to verify before approving one.
ViewCompliance / 9 min read
AI-generated code and open-source licenses: managing the IP risk
How AI coding tools create open-source license and IP risk, and the practical controls teams use to manage code provenance without slowing delivery.
ViewEnablement / 8 min read
Building an AI coding center of excellence that engineers actually use
A practical guide to standing up an AI coding center of excellence: what it owns, how to staff it without bureaucracy, and how to scale adoption.
ViewOperations / 11 min read
Governing AI coding agents in production
How engineering leaders keep autonomous coding agents safe in production: scope, guardrails, review, and clear accountability.
ViewRisk / 10 min read
Incident response when AI-generated code fails in production
A practical playbook for engineering leaders: triage, ownership, root cause, and prevention when AI-assisted code causes a production incident.
ViewStrategy / 8 min read
AI coding tool lock-in: staying portable as you scale
What actually creates lock-in with AI coding tools, what it costs, and how to keep an exit option open while you commit.
ViewEnablement / 8 min read
What AI coding tools do to junior developers, and how to protect their growth
AI makes juniors productive on day one and can stall the learning that makes them seniors. How to get the speed without hollowing out your future senior bench.
ViewQuality / 8 min read
Quality gates for AI-generated code: what belongs in your CI pipeline
Which automated gates actually catch AI-specific failures, and which give false confidence once a model is writing more of your code.
ViewSecurity / 9 min read
How to security-review an AI coding tool before you roll it out
What to ask vendors about data handling, training, access, and compliance before you put an AI coding tool in front of your engineers.
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